Design of Spatial-Spectral Filters for CT Material Decomposition

Abstract

Spectral CT has shown promise for high-sensitivity quantitative imaging and material decomposition. This work presents a new device called a spatial-spectral filter (SSF) which consists of a tiled array of filter materials positioned near the x-ray source that is used to modulate the spectral shape of the x-ray beam. The filter is moved to obtain projection data that is sparse in each spectral channel. To process this sparse data, we employ a direct model-based material decomposition (MBMD)to reconstruct basis material density images directly from the SSF CT data. To evaluate different possible SSF designs, we define a new Fisher-information-based predictive image quality metric called separability index which characterizes the ability of a spectral CT system to distinguish between the signals from two or more materials. This predictive metric is used to define a system design optimization framework. We have applied this framework to find optimized combinations of filter materials, filter tile widths, and source settings for SSF CT. We conducted simulation-based design optimization study and separability-optimized filter designs are presented for water/iodine imaging and water/iodine/gadolinium/gold imaging for different patient sizes. Finally, we present MBMD results using simulated SSFCT data using the optimized designs to demonstrate the ability to reconstruct basis material density images and to show the benefits of the optimized designs.